Measuring caloric response: Comparison of different analysis techniques
Bibliographic record
Abstract
INTRODUCTION: Electronystagmography (ENG) testing has been supplanted by newer techniques of measuring eye movement with infrared cameras (VNG). Most techniques of quantifying caloric induced nystagmus measure the slow phase velocity in some manner. Although our analysis is carried out by very experienced assessors, some systems have computer algorithms that have been "taught" to locate and quantify maximum responses. We wondered what differences in measurement might show up when measuring calorics using different techniques and systems, the relevance of this being that if there was a change in slow phase velocity between ENG and VNG testing when measuring caloric response, then normative data would have to be changed. There are also some subjective but important aspects of ENG interpretation which comment on the nature of the response (e.g. responses which might be "sporadic" or "scant"). METHODS: Our experiment compared caloric responses in 100 patients analyzed four different ways. Each caloric was analyzed by our old ENG system, our new VNG system, an inexperienced assessor and the computer algorithm, and data was compared. CONCLUSIONS: All four systems made similar measurements but our inexperienced assessor failed to recognize responses as sporadic or scant, and we feel this is a limitation to be kept in mind in the rural setting, as it is an important aspect of assessment in complex patients. Assessment of complex VNGs should be left to an experienced assessor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".